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Towards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases / Johanna Guth in Journal of Spatial Information Science (JoSIS), n° 22 (2021)  

Public [article]  inJournal of Spatial Information Science (JoSIS) > n° 22 (2021) . - pp 1 - 31 Titre : Towards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases Type de document : Article/Communication Auteurs : Johanna Guth, Auteur ; Sina Keller, Auteur ; Stefan Hinz, Auteur ; Stephan Winter, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 31 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] base de données localisées [Termes IGN] détection d'erreur [Termes IGN] données localisées des bénévoles [Termes IGN] OpenStreetMap [Termes IGN] qualité des données Résumé : (auteur) OpenStreetMap (OSM), with its global coverage and Open Database License, has recently gained popularity. Its quality is adequate for many applications, but since it is crowd-sourced, errors remain an issue. Errors in associated tags of the road network, for example, are impacting routing applications. Particularly road classification errors often lead to false assumptions about capacity, maximum speed, or road quality, possibly resulting in detours for routing applications. This study aims at finding potential classification errors automatically, which can then be checked and corrected by a human expert. We develop a novel approach to detect road classification errors in OSM by searching for disconnected parts and gaps in different levels of a hierarchical road network. Different parameters are identified that indicate gaps in road networks. These parameters are then combined in a rating system to obtain an error probability to suggest possible misclassifications to a human user. The methodology is applied to an exemplar case for the state of New South Wales in Australia. The results demonstrate that (1) more classification errors are found at gaps than at disconnected parts, and (2) the gap search enables the user to find classification errors quickly using the developed rating system that indicates an error probability. In future work, the methodology can be extended to include available tags in OSM for the rating system. The source code of the implementation is available via GitHub. Numéro de notice : A2021-728 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5311/JOSIS.2021.22.677 Date de publication en ligne : 30/06/2021 En ligne : https://doi.org/10.5311/JOSIS.2021.22.677 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98660 [article]

Rethinking error estimations in geospatial data: the correct way to determine product accuracy / Qassim Abdullah in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 7 (July 2020) . - pp 397-403 Titre : Rethinking error estimations in geospatial data: the correct way to determine product accuracy Type de document : Article/Communication Auteurs : Qassim Abdullah, Auteur Année de publication : 2020 Article en page(s) : pp 397-403 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie moderne [Termes IGN] cartographie numérique [Termes IGN] détection d'erreur [Termes IGN] données localisées [Termes IGN] ellipsoïde (géodésie) [Termes IGN] estimation de précision [Termes IGN] nivellement direct [Termes IGN] point d'appui [Termes IGN] réalité de terrain [Termes IGN] système de référence géodésique [Termes IGN] système de référence local Résumé : (Auteur) Numéro de notice : A2020-380 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.7.397 Date de publication en ligne : 01/07/2020 En ligne : https://doi.org/10.14358/PERS.86.7.397 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95425 [article]

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Assessment of ArcGIS based extraction of geoidal undulation compared to National Geospatial Intelligence Agency (NGA) model – A case study / Sher Muhammad in Journal of applied geodesy, vol 14 n° 1 (January 2020)  

Public [article]  inJournal of applied geodesy > vol 14 n° 1 (January 2020) . - pp 77 - 81 Titre : Assessment of ArcGIS based extraction of geoidal undulation compared to National Geospatial Intelligence Agency (NGA) model – A case study Type de document : Article/Communication Auteurs : Sher Muhammad, Auteur ; Lide Tian, Auteur Année de publication : 2020 Article en page(s) : pp 77 - 81 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] altitude orthométrique [Termes IGN] détection d'erreur [Termes IGN] Earth Gravity Model 1996 [Termes IGN] ellipsoïde (géodésie) [Termes IGN] géoïde altimétrique [Termes IGN] Himalaya [Termes IGN] interpolation [Termes IGN] Matlab [Termes IGN] modèle numérique de surface [Termes IGN] niveau moyen des mers [Termes IGN] Tibet Résumé : (auteur) Global Navigation Satellite System (GNSS) and remote sensing Digital Elevation Models (DEMs) represent earth’s surface elevation with reference to ellipsoid and orthometric heights. Proper estimation of the geoid (difference of ellipsoid and orthometric heights) is necessary before comparing data referenced to the different vertical datum. In this paper, an error in estimating EGM96 orthometric height is highlighted, verified by NGA/NASA developed model and MATLAB®. A significant error was found in the ArcGIS derived EGM96 orthometric heights range between ±6.9 meters. In addition, interpolation of low-resolution geoid data also produces significant biases depending on geographic location and the number of the interpolation data point. The bias was maximum negative in the central part of Tibetan Plateau and Himalaya. Therefore, estimation of orthometric height similar to NGA/NASA model precision is necessary for comparison of DEMs for natural resources management, 3D modelling and glaciers mass balance mainly in the mountainous regions. Numéro de notice : A2020-041 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2019-0030 En ligne : https://doi.org/10.1515/jag-2019-0030 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94512 [article]

Assessment of inner reliability in the Gauss-Helmert model / Andreas Ettlinger in Journal of applied geodesy, vol 14 n° 1 (January 2020)  

Public [article]  inJournal of applied geodesy > vol 14 n° 1 (January 2020) . - pp 13 - 28 Titre : Assessment of inner reliability in the Gauss-Helmert model Type de document : Article/Communication Auteurs : Andreas Ettlinger, Auteur ; Hans Neuner, Auteur Année de publication : 2020 Article en page(s) : pp 13 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie [Termes IGN] coefficient de corrélation [Termes IGN] compensation par moindres carrés [Termes IGN] détection d'erreur [Termes IGN] erreur systématique [Termes IGN] fiabilité des données [Termes IGN] filtre de Kalman [Termes IGN] modèle d'erreur [Termes IGN] modèle de Gauss-Helmert [Termes IGN] valeur aberrante Résumé : (auteur) In this contribution, the minimum detectable bias (MDB) as well as the statistical tests to identify disturbed observations are introduced for the Gauss-Helmert model. Especially, if the observations are uncorrelated, these quantities will have the same structure as in the Gauss-Markov model, where the redundancy numbers play a key role. All the derivations are based on one-dimensional and additive observation errors respectively offsets which are modeled as additional parameters to be estimated. The formulas to compute these additional parameters with the corresponding variances are also derived in this contribution. The numerical examples of plane fitting and yaw computation show, that the MDB is also in the GHM an appropriate measure to analyze the ability of an implemented least-squares algorithm to detect if outliers are present. Two sources negatively influencing detectability are identified: columns close to the zero vector in the observation matrix B and sub-optimal configuration in the design matrix A. Even if these issues can be excluded, it can be difficult to identify the correct observation as being erroneous. Therefore, the correlation coefficients between two test values are derived and analyzed. Together with the MDB these correlation coefficients are an useful tool to assess the inner reliability – and therefore the detection and identification of outliers – in the Gauss-Helmert model. Numéro de notice : A2020-040 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2019-0013 Date de publication en ligne : 19/10/2019 En ligne : https://doi.org/10.1515/jag-2019-0013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94511 [article]

A learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)    

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 12 (December 2019) . - pp 865 - 878 Titre : A learning approach to evaluate the quality of 3D city models Type de document : Article/Communication Auteurs : Oussama Ennafii , Auteur ; Arnaud Le Bris , Auteur ; Florent Lafarge, Auteur ; Clément Mallet , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 865 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Bâti-3D [Termes IGN] classification par forêts aléatoires [Termes IGN] détection d'erreur [Termes IGN] données localisées [Termes IGN] France (administrative) [Termes IGN] image à très haute résolution [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle d'erreur [Termes IGN] modèle numérique de surface [Termes IGN] qualité des données [Termes IGN] taxinomie Résumé : (Auteur) The automatic generation of three-dimensional (3D) building models from geospatial data is now a standard procedure. An abundance of literature covers the last two decades, and several solutions are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at a city-scale, the correctness of these models and detect frequent reconstruction errors. Such a process relies on experts and is highly time-consuming, with approximately two hours/km 2 per expert. This work proposes an approach for automatically evaluating the quality of 3D building models. Potential errors are compiled in a novel hierarchical and versatile taxonomy. This allows, for the first time, to disentangle fidelity and modeling errors, whatever the level of details of the modeled buildings. The quality of models is predicted using the geometric properties of buildings and, when available, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yet generic, features is fed into a Random Forest classifier. Both multiclass and multilabel cases are considered: due to the interdependence between classes of errors, it is possible to retrieve all errors at the same time while simply predicting correct and erroneous buildings. The proposed framework was tested on three distinct urban areas in France with more than 3000 buildings. 80%–99% F-score values are attained for the most frequent errors. For scalability purposes, the impact of the urban area composition on the error prediction was also studied, in terms of transferability, generalization, and representativeness of the classifiers. It showed the necessity of multimodal remote sensing data and mixing training samples from various cities to ensure a stability of the detection ratios, even with very limited training set sizes. Numéro de notice : A2019-569 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.12.865 Date de publication en ligne : 01/12/2019 En ligne : https://doi.org/10.14358/PERS.85.12.865 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94440 [article]

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